PEFN: A Patches Enhancement and Hierarchical Fusion Network for Robust Vehicle Reidentification

Vehicle reidentification (Re-ID), which is a significant application in the Internet of Things, aims to accurately retrieve the remaining images of a given vehicle across different cameras views. The improvement in vehicle Re-ID performance largely stems from better addressing the issues of interclass similarity and intraclass variance. Existing methods, relying solely on max or average pooling after using attention modules, fail to obtain significantly complete and pure global and local features, and neglect the false guidance that some unique individual information on images bring to Re-ID. Moreover, models combining global and local features have shown good results in vehicle Re-ID, but these successes neglect the interaction between features across different convolutional layers, resulting in the loss of crucial details for vehicle Re-ID. To tackle these issues, we introduce a patches enhancement and hierarchical fusion network (PEFN) based on a multibranch architecture, divided into a global and local attention supplement (GLAS) branch, and an enhanced hierarchical feature fusion (EnHi) branch. The GLAS branch, through the identity-related feature remodeling (IDFR) module’s staged supplementation of spatial and channel features, has achieved the enhancement of both global and local features and effectively mitigated the negative impacts of individual information. The EnHi branch enhances the robustness of feature representation by interacting hierarchical features. Extensive experiments on two large-scale vehicle Re-ID datasets demonstrate that our PEFN method outperforms state-of-the-art vehicle Re-ID approaches. Specifically, without utilizing extra data and reranking, our model achieves 85.15% mean average precision on the VeRi776 dataset. Code is available at https://github.com/711L/PEFN.